Capsule endoscope for determining lesion area and receiving device

ABSTRACT

Provided is a capsule endoscope. The capsule endoscope includes: an imaging device configured to perform imaging on a digestive tract in vivo to generate an image; an artificial neural network configured to determine whether there is a lesion area in the image; and a transmitter configured to transmit the image based on a determination result of the artificial neural network.

CROSS-REFERENCE TO RELATED APPLICATIONS

This U.S. non-provisional patent application claims priority under 35U.S.C. § 119 of Korean Patent Application Nos. 10-2017-0075090, filed onJun. 14, 2017, and 10-2017-0114741, filed on Sep. 7, 2017, the entirecontents of which are hereby incorporated by reference.

BACKGROUND

The present disclosure relates to a capsule endoscope and a receivingdevice, and more particularly, to a capsule endoscope and a receivingdevice for determining a lesion area.

To examine a digestive tract in a living body, a swallowable capsuleendoscope instead of a wired endoscope is being used. A capsuleendoscope may identify the digestive tract without the inconvenience ofa wired endoscope. The capsule endoscope may perform imaging whilepassing through the digestive tract such as the stomach, duodenum, smallintestine, colon, and the like. The capsule endoscope may transmitimages generated by imaging to a receiving device outside a body, andthe receiving device may store the images.

The capsule endoscope continuously performs imaging and transmits imageswhile passing through the digestive tract from the mouth to the anus.The capsule endoscope is usually made in pill sizes for ingestion, so acapacity and a size of a battery loaded on the capsule endoscope arelimited. Therefore, a technique for reducing the power consumption ofthe capsule endoscope is required.

SUMMARY

The present disclosure is to provide a capsule endoscope for determininga lesion area and a receiving device.

An embodiment of the inventive concept provides a capsule endoscopeincluding: an imaging device configured to perform imaging on adigestive tract in a living body to generate an image; an artificialneural network configured to determine whether there is a lesion area inthe image; and a transmitter configured to transmit the image based on adetermination result of the artificial neural network.

In an embodiment of the inventive concept, a receiving device includes:a receiver configured to receive from a capsule endoscope an image of adigestive tract in a living body and a flag bit indicating whether thereis a lesion area in the image; a decoder configured to decode the flagbit and determine whether to store the image; and a storage deviceconfigured to store the image in accordance with a decoding result ofthe decoder.

BRIEF DESCRIPTION OF THE FIGURES

The accompanying drawings are included to provide a furtherunderstanding of the inventive concept, and are incorporated in andconstitute a part of this specification. The drawings illustrateexemplary embodiments of the inventive concept and, together with thedescription, serve to explain principles of the inventive concept. Inthe drawings:

FIG. 1 is a view illustrating an exemplary capsule endoscope systemaccording to an embodiment of the inventive concept;

FIG. 2 is a block diagram illustrating an exemplary capsule endoscope ofFIG. 1;

FIG. 3 is a block diagram illustrating exemplary detail layers of theartificial neural network of FIG. 2;

FIG. 4 is a diagram illustrating a process of analyzing an image throughthe detail layers of FIG. 3;

FIG. 5 is a block diagram illustrating exemplary detail layers of theartificial neural network of FIG. 2.

FIG. 6 is a diagram illustrating an example in which the detail layersof FIG. 5 are implemented.

FIG. 7 is a view showing a process of passing the capsule endoscope ofFIG. 2 through a digestive tract;

FIG. 8 is a diagram illustrating an exemplary packet transmitted by thecapsule endoscope of FIG. 2;

FIG. 9 is a block diagram illustrating an exemplary capsule endoscopeaccording to another embodiment of the inventive concept;

FIG. 10 is a block diagram illustrating a capsule endoscope, a receivingdevice, and a capsule endoscope system according to an embodiment of theinventive concept;

FIG. 11 is a flowchart illustrating exemplary operations of the capsuleendoscope and the receiving device of FIG. 10; and

FIG. 12 is a flowchart illustrating exemplary operations of the capsuleendoscope and the receiving device of FIG. 10.

DETAILED DESCRIPTION

In the following, embodiments of the inventive concept will be describedin detail so that those skilled in the art easily carry out theinventive concept.

FIG. 1 is a view illustrating an exemplary capsule endoscope systemaccording to an embodiment of the inventive concept. Referring to FIG.1, a capsule endoscope system 10 may include a capsule endoscope 100 anda receiving device 300.

The capsule endoscope 100 may pass through the digestive tract 11 in aliving body. The digestive tract 11 may be referred to as a digestiveorgan. The living body may be referred to as a body. The capsuleendoscope 100 may perform imaging on the digestive tract 11 and mayproduce images for the digestive tract 11. The capsule endoscope 100 maytransmit the generated images to the receiving device 300 outside theliving body. The transmission may be performed through wirelesscommunication or human body communication using the human body as amedium.

According to an embodiment of the inventive concept, in order to reducepower consumption, the capsule endoscope 100 may transmit only a validimage of the generated images to the receiving device 300 instead oftransmitting all the generated images to the receiving device 300. Here,the valid image represents an image having a lesion area in which thelesion of the digestive tract 11 may be suspected. Since the capsuleendoscope 100 transmits only the valid image, power consumption requiredfor transmission may be reduced.

According to another embodiment of the inventive concept, in order toreduce the amount of images generated by imaging, the capsule endoscope100 may generate flag bits for each of the generated images. Here, theflag bits may indicate whether the images are valid images,respectively.

The receiving device 300 may store images transmitted from the capsuleendoscope 100. For example, the receiving device 300 may be anelectronic device such as a computer, a mobile device, a smart phone, awearable device, a server, etc., capable of receiving, storing, ordisplaying an image. In an embodiment, the receiving device 300 mayreceive and store only the valid image from the capsule endoscope 100.In another embodiment, the receiving device 300 may store only the validimage of the received images using the flag bits described above. Thereceiving device 300 may filter the received images using flag bits.Thus, the power consumption required to store an image in the receivingdevice 300 may be reduced.

FIG. 2 is a block diagram illustrating an exemplary capsule endoscope ofFIG. 1. Referring to FIG. 2, the capsule endoscope 100 may include animaging device 110, an artificial neural network 120, a transmitter 130,and output ports 141 and 142.

The imaging device 110 may include, for example, an image sensor such asa charge coupled device (CCD) image sensor or a complementary metaloxide semiconductor (CMOS) image sensor. After the light is projectedfrom the light source (not shown) in the capsule endoscope 100 to thedigestive tract 11, the image sensor may sense the reflected light andgenerate an electrical signal. The image sensor may perform imaging onthe digestive tract 11 and may produce an image (or image data). Thegenerated image may be provided to the artificial neural network 120.

The artificial neural network 120 may determine whether there is alesion area in the image generated by the imaging device 110. Theartificial neural network 120 may be based on a deep learning engine andmay more specifically be based on a convolutional neural network (CNN)used in image analysis.

The artificial neural network 120 may provide a valid image having alesion area to the transmitter 130. Then, the artificial neural network120 may generate a control signal for controlling the transmitter 130based on the image determination result. The artificial neural network120 may determine whether to activate the transmitter 130 or to supplypower to the transmitter 130 using a control signal. The artificialneural network 120 may activate the transmitter 130 or may supply orprovide power to the transmitter 130 when a valid image is transmittedto the transmitter 130. The artificial neural network 120 may deactivatethe transmitter 130 or may not supply power to the transmitter 130 whena valid image is not transmitted to the transmitter 130. Therefore, thepower consumption of the transmitter 130 may be reduced.

Referring to FIG. 2, the artificial neural network 120 may include orstore a kernel matrix and a weight. Here, the kernel matrix and theweight may be used in the determination process of the artificial neuralnetwork 120, and may be data previously learned through machinelearning. The kernel matrix and the weight may be updated as a result oflearning whether the artificial neural network 120 has a lesion area inthe image and may be included or stored back in the artificial neuralnetwork 120. A detailed image determination process by the artificialneural network 120 will be described later with reference to FIG. 3 toFIG. 7.

In an embodiment, for example, the artificial neural network 120 may beimplemented in hardware within the capsule endoscope 100. The artificialneural network 120 may be implemented as a system-on-chip (SoC), anapplication specific integrated circuit (ASIC), or a field programmablegate array (FPGA). For example, the artificial neural network 120 mayinclude a central processing unit (CPU), a graphical processing unit(GPU), a micro processing unit (MPU), and the like.

The transmitter 130 may transmit the image to the receiving device (seethe receiving device 300 of FIG. 1) outside a living body based on thedetermination result of the artificial neural network 120. Thetransmitter 130 may operate depending on a control signal of theartificial neural network 120. The transmitter 130 may convert thedigital signal constituting the image into a signal for transmission.For this, the transmitter 130 may include a frame generator 131, aprotocol generator 132, and a signal generator 133.

The frame generator 131 may generate a frame of a packet fortransmitting an image. More specifically, the frame generator 131 maydetermine where in the packet the information of the transmitter 130,the information of the receiving device, the data according to themutually agreed protocol between the transmitter 130 and the receivingdevice, the image data, and the like are located in the packet.

The protocol generator 132 may generate data according to a mutuallyagreed protocol between the transmitter 130 and the receiving device. Inan embodiment, the protocol generator 132 may generate data forperforming wireless communication such as wi-fe, wigig, wibro, wimax,radio frequency identification (RFID), bluetooth, zigbee, ultra wideband (UWB), and the like. In another embodiment, the protocol generator132 may generate data for performing human body communication with areceiving device attached to the human body.

The signal generator 133 may convert information of the transmitter 130,information of the receiving device, and data according to mutuallyagreed protocols between the transmitter 130 and the receiving device,and image data in the form of digital signals into analog signals. Thesignal generator 133 may provide analog signals to the output ports 141and 142.

The output ports 141 and 142 may output an analog signal to thereceiving device. In an embodiment, the output ports 141 and 142 may beantennas capable of transmitting analog signals according to wirelesscommunication. In other embodiments, the output ports 141 and 142 may beelectrodes capable of transmitting analog signals according to humanbody communication. In this case, the electric current generateddepending on the potential difference between the electrodes may betransmitted to the receiving device through the human body.

FIG. 3 is a block diagram illustrating exemplary detail layers of theartificial neural network of FIG. 2. FIG. 4 is a diagram illustrating aprocess of analyzing an image through the detail layers of FIG. 3. FIGS.3 and 4 will be described together. The artificial neural network ofFIGS. 3 and 4 may be based on the CNN.

Referring to FIG. 3, the artificial neural network 120 includesconvolutional layers 121_1 to 121_n, maxpooling layers 122_1 to 122_n,and a fully connected layer 123. Here, n is a natural number and may bedetermined in advance considering the determination accuracy of theartificial neural network 120, the determination time of the artificialneural network 120, and the like. Each of the convolutional layers 121_1to 121_n and each of the maxpooling layers 122_1 to 122_n may bealternately arranged.

The artificial neural network 120 may analyze the image and extractfeatures in the image through the convolutional layers 121_1 to 121_nand the maxpooling layers 122_1 to 122_n. The artificial neural network120 may classify the image through the fully connected layer 123. Thatis, the artificial neural network 120 may determine whether there is alesion area in the image.

Referring to FIG. 4, an exemplary process for analyzing an image when nis 2 is illustrated. In the first convolutional layer 121_1, aconvolutional operation on the image and kernel matrix 124_1 may beperformed. More specifically, a convolutional operation is performed onthe kernel matrix 124_1 and the image area where the kernel matrix 124_1are located in the image. The convolutional result 124_2 may indicatehow similar the image area is to the kernel matrix 124_1. For example,as the convolutional result 124_2 is greater, the degree of similaritybetween the image area and the kernel matrix 124_1 becomes greater. Thekernel matrix 124_1 may be moved by stride in the image and theconvolutional operation may be iteratively performed so that the entireimage may be scanned or filtered. Convolutional results may be gatheredand new images may be generated. Here, the number of new images may bedetermined according to the number of kernel matrices, and the size ofthe new images may be determined by the size of the image generated bythe imaging device 110 (see FIG. 2), the size of the kernel matrix,strides, padding, and the like.

In the first maxpooling layer 122_1, sampling on the new images may beperformed. The pixel 124_4 having the maximum value of the pixels in thesampling filter 124_3 may be sampled. Similar to the convolutionaloperation, the sampling filter 124_3 may be moved by stride in the imagecontaining the convolutional results, and the sampling may be performediteratively. Pixels having the maximum value may be gathered and newimages may be generated. Here, as the sampling is performed, the size ofthe image may be reduced. Operations in the second convolutional layer121_2 and the second maxpooling layer 122_2 are substantially similar tooperations in the first convolutional layer 121_1 and the firstmaxpooling layer 122_1. That is, the image may pass through at least oneconvolutional layer and at least one maxpooling layer, and features ofthe image may be extracted in the pass-through process.

In the fully connected layer 123, the results of passing through thefirst convolutional layer 121_1, the first maxpooling layer 122_1, thesecond convolutional layer 121_2, and the second maxpooling layer 122_2may be classified. The fully connected layer 123 may include artificialneurons, and the artificial neurons may be connected through a synapse.Herein, the intensity of the synapse or the degree of coupling ofartificial neurons may be determined by the weights included or storedin the artificial neural network 120. The artificial neural network 120may determine whether the image includes lesions such as bleeding,polyps, and the like.

FIG. 5 is a block diagram illustrating exemplary detail layers of theartificial neural network of FIG. 2. FIG. 6 is a diagram illustrating anexample in which the detail layers of FIG. 5 are implemented. FIGS. 5and 6 will be described together. The artificial neural network 120 ofFIGS. 5 and 6 may be based on spiking neural network (SNN). The SNN mayuse independent spikes and may be simply implemented than the CNN. Incomparison with the CNN, since the SNN may have a simple structure,power consumption and of SNN may be lower than power consumption and ofCNN and an area of SNN may be smaller than an area of the CNN.

Referring to FIG. 5, the artificial neural network 120 may include aninput layer 125, a hidden layer 126, and an output layer 127. The inputlayer 125 may receive image generated by the imaging device 110. Each ofartificial neurons of the input layer 125 may output spikes toartificial neurons of the hidden layer 126 based on the image. Each ofthe artificial neurons of the hidden layer 126 may determine whether tooutput spikes to the output layer 127, based on the spikes received fromthe input layer 125. For example, each of the artificial neurons of thehidden layer 126 may accumulate the spikes received from the input layer125 and may output spikes to the output layer 127 when the accumulationresult reaches a threshold value.

For brevity of illustration, it is illustrated that the number of hiddenlayer 126 is one in FIG. 5, but the number of hidden layer 126 may beone or more. In this case, each of the artificial neurons of the hiddenlayer 126 may output spikes to another hidden layer (not shown) based onthe spikes received from the input layer 125. Further, the number ofartificial neurons included in each of the layers 125, 126, and 127 isonly exemplary.

The output layer 127 may output whether there is a lesion area in theimage generated by the imaging device 110. For example, the output layer127 may output that the image input to the input layer 125 correspondsto a normal area. The output layer 127 may output that the image inputto the input layer 125 corresponds to a lesion area. For example, anaccuracy of determination results (outputs of the output layer 127)based on the SNN may be lower than an accuracy of determination results(outputs of the fully connected layer 123) based on the CNN. If there isa lesion area in the image, the CNN may perform a diagnosis on thelesion area. That is, the CNN may determine diagnostic informationrelated to the lesion area. However, the SNN may only determine whetherthe image corresponds to a normal area or a lesion area. Further, theSNN may determine whether there is a suspect lesion area in the image.Although an accuracy of determination of the SNN may be lower than theCNN, the capsule endoscope 100 may only transmit an image having alesion area or an image having a suspect lesion area by using theartificial neural network 120 based on the SNN.

In FIG. 6, an example in which the artificial neural network 120 basedon the SNN is implemented in hardware manner is illustrated. Forexample, artificial neurons 128 of the layers 125, 126, and 127 may bearranged along the X-axis or Y-axis. Each of synapses 129 located at theinterconnections of the X-axis and Y axis may indicate the degree ofcoupling of the artificial neurons 128. The synapses 129 may bedetermined by weights included or stored in the artificial neuralnetwork 120. The number of synapses 129 in FIG. 6 is not limitedthereto.

FIG. 7 is a view showing a process of passing the capsule endoscope ofFIG. 2 through a digestive tract. In FIG. 7, the horizontal axisrepresents a time, and it is assumed that the lesions 12 and 13 arepresent in the digestive tract 11.

The capsule endoscope 100 may generate images 161 to 165 while passingthrough the digestive tract 11. The artificial neural network 120 maydetermine a valid image 163 having a lesion area of the images 161 to165. Then, the artificial neural network 120 may generate a controlsignal to control the transmitter 130. For example, the artificialneural network 120 may set the control signal to logic 0 duringintervals corresponding to images 161, 162, 164, and 165, and may setthe control signal to logic 1 during the interval corresponding to thevalid image 163. Here, the logic states of the control signal may be setas opposed to those shown in the drawing. The transmitter 130 mayoperate only in the interval corresponding to the valid image 163depending on the control signal.

FIG. 8 is a diagram illustrating an exemplary packet transmitted by thecapsule endoscope of FIG. 2. FIG. 8 relates to the case where thecapsule endoscope 100 transmits the images 161 to 165 as well as thevalid image 163 to a receiving device.

The artificial neural network 120 may determine whether there is alesion area in each of the images 161 to 165. The artificial neuralnetwork 120 may provide the transmitter 130 with the determinationresults for the images 161 to 165 and the images 161 to 165,respectively. The transmitter 130 may generate flag bits, eachrepresenting the determination results of the artificial neural network120, and may transmit the flag bits with the images 161 to 165 to thereceiving device. For example, the value of the flag bit may be logic 1if the corresponding image is a valid image and may be logic 0 if theimage is not a valid image.

In an embodiment, the frame generator 131 of the transmitter 130 maydetermine the location of the flag bits and the location of the imagedata. Referring to FIG. 8, the frame generator 131 may determine thelocation of a flag bit such that a flag bit may be transmitted to thereceiving device before the image data.

FIG. 9 is a block diagram illustrating an exemplary capsule endoscopeaccording to another embodiment of the inventive concept. Referring toFIG. 9, the capsule endoscope 200 may include an imaging device 210, anartificial neural network 220, a transmitter 230, output ports 241 and242, a power supply circuit 250, and a switch 260. Here, the imagingdevice 210, the artificial neural network 220, the transmitter 230, andthe output ports 241 and 242 perform the same functions as the imagingdevice 110, the artificial neural network 120, the transmitter 130, andthe output ports 141 and 142 of the FIG. 2, respectively. The differencebetween the capsule endoscope 200 and the capsule endoscope 100 will bedescribed below.

The power supply circuit 250 may supply power to the components of thecapsule endoscope 200. For example, the power supply circuit 250 may bea battery. Referring to FIG. 9, although the power supply circuit 250 isillustrated as supplying power only to the transmitter 230 through theswitch 260, the power supply circuit 250 may also supply power to theimaging device 210 and the artificial neural network 220.

The switch 260 may be turned on or turned off depending on the controlsignal of the artificial neural network 220. For example, if theartificial neural network 220 determines that there is a lesion area inthe image of the imaging device 210 and activates the control signal,the switch 260 may be turned on, and if not, may be turned off. When theswitch 260 is turned off, since the transmitter 230 does not transmit animage other than a valid image, the power consumption of the capsuleendoscope 200 may be reduced.

FIG. 10 is a block diagram illustrating a capsule endoscope, a receivingdevice, and a capsule endoscope system according to an embodiment of theinventive concept. A capsule endoscope system 1000 may include a capsuleendoscope 1100 and a receiving device 1300. The capsule endoscope 1100may be the capsule endoscope 100 of FIG. 2 or the capsule endoscope 200of FIG. 9. Here, the imaging device 1110, the artificial neural network1120, the transmitter 1130, and the output ports 1141 and 1142 performthe substantially same functions as the imaging device 110, theartificial neural network 120, the transmitter 130, and the output ports141 and 142, respectively. The receiving device 1300 may include inputports 1311 and 1312, a receiver 1320, a decoder 1330, a switch 1340, anda storage device 1350.

In an embodiment, the input ports 1311 and 1312 may be antennas capableof receiving analog signals according to wireless communication. Inother embodiments, the input ports 1311 and 1312 may be electrodescapable of receiving analog signals according to human bodycommunication. In this case, the input ports 1311 and 1312 may beattached to the human body.

The receiver 1320 may receive an image in the form of an analog signalfrom the capsule endoscope 1100 through the input ports 1311 and 1312.The receiver 1320 may convert the analog signal to a digital signalaccording to a protocol previously agreed with the transmitter of thecapsule endoscope 1100. During the conversion process, the receiver 1320may amplify and filter the analog signal. The receiver 1320 may providethe storage device 1350 with an image transmitted by the capsuleendoscope 1100.

In an embodiment, the capsule endoscope 1100 may transmit a valid imagehaving a lesion area to the receiving device 1300. In this case, thevalid image may be stored in the storage device 1350 as it is, and thereceiving device 1300 may not include the decoder 1330 and the switch1340 as shown in the drawing.

In another embodiment, the capsule endoscope 1100 may transmit to thereceiving device 1300 images and flag bits indicating whether each ofthe images is a valid image, like a packet of FIG. 8. In this case, thedecoder 1330 may decode the flag bits and determine whether to store theimages. The decoder 1330 may control the switch 1340 based on thedecoding result.

If the flag bit indicates that the image is a valid image, the switch1340 may be turned on, or if not, may be turned off. Accordingly, onlythe valid image of the received images may be provided to the storagedevice 1350, and other images other than the valid image may not beprovided.

The storage device 1350 may store the image depending on the decodingresult of the decoder 1330. A valid image having a lesion area of theimages received from the capsule endoscope 1100 may be stored in thestorage device 1350 and other images other than the valid image may notbe stored in the storage device 1350. If the receiver 1320 receives onlythe valid image from the capsule endoscope 1100, the storage device 1350may store the valid image received by the receiver 1320 as it is.

The storage device 1350 may be any of a variety of storage devicesincluding, for example, a dynamic random access memory (DRAM), a staticrandom access memory (SRAM), a read only memory (ROM), a programmableROM (PROM), an electrically programmable ROM (EPROM), an electricallyerasable and programmable ROM (EEPROM), a solid state drive (SSD), ahard disk drive (HDD), a NAND flash memory, a NOR flash memory, amagnetic random access memory (MRAM), a phase-change random accessmemory (PRAM), a ferroelectric random access memory (FRAM), a thyristorrandom access memory (TRAM), and the like.

FIG. 11 is a flowchart illustrating exemplary operations of the capsuleendoscope and the receiving device of FIG. 10. Referring to FIG. 11,operations S110 to S150 are the operations of the capsule endoscope1100, and operation S160 is the operation of the receiving device 1300.

In operation S110, it may be determined whether the capsule endoscope1100 is powered on or whether the battery is capable of supplying power.If the power is on (Yes), operation S120 proceeds, and if the power isoff (No), the capsule endoscope 1100 no longer operates.

In operation S120, the capsule endoscope 1100 may perform imaging on thedigestive tract and may generate an image. The period and interval ofthe imaging of the capsule endoscope 1100 may be predetermined. In anembodiment, the period and interval of the imaging may vary depending onthe speed of movement of the capsule endoscope 1100. In anotherembodiment, in order for the capsule endoscope to produce an image for aparticular digestive tract, performing the imaging may be determineddepending on the position of the capsule endoscope 1100 or the elapsedtime after the capsule endoscope 1100 is inserted into the human body.

In operation S130, the capsule endoscope 1100 may determine whetherthere is a lesion area in the image generated in operation S120. Forthis purpose, the capsule endoscope 1100 may include an artificialneural network 1120 based on CNN or an artificial neural network 1120based on SNN. In the artificial neural network 1120, a previouslylearned kernel matrix and weight may be stored to determine an image.

In operation S140, if there is a lesion area in the image generated inoperation S120 (Yes), operation S150 is performed. If there is no lesionarea in the image (No), operation S110 is performed.

In operation S150, the capsule endoscope 1100 may transmit a valid imagehaving a lesion area of the images regularly or irregularly generated inoperation S120 to the receiving device 1300. Then, the capsule endoscope1100 may perform operation S110 again. In operations other thanoperation S150, the transmitter transmitting the image may bedeactivated or power may not be supplied to the transmitter.

In operation S160, the receiving device 1300 may receive and store theimage. The image received in operation S160 is a valid image. Since onlythe valid image is stored in the receiving device 1300, the storagecapacity of the receiving device 1300 may decrease. Also, the amount ofimage that a user (e.g., a doctor) has to determine may be reduced andthe determination time may be reduced.

FIG. 12 is a flowchart illustrating exemplary operations of the capsuleendoscope and the receiving device of FIG. 10. Referring to FIG. 12,operations S210 to S250 are operations of the capsule endoscope 1100,and operations S260 to S280 are operations of the receiving device 1300.In FIG. 12, operation S210, operation S220, and operation S230 aresubstantially the same as operation S110, operation S120, and operationS130 in FIG. 11, respectively.

In operation S240, the capsule endoscope 1100 may generate a flag bitbased on the determination result of operation S230. The flag bit mayindicate the determination result of operation S230. That is, the flagbit may indicate whether the image generated in operation S220 is avalid image.

In operation S250, the capsule endoscope 1100 may transmit the imagegenerated in operation S220 and the flag bit generated in operation S240to the receiving device 1300. Then, the capsule endoscope 1100 mayperform operation S210 repeatedly.

In operation S260, the receiving device 1300 may receive the image andthe flag bit. In operation S270, the receiving device 1300 may determinethrough the flag bit whether the image has the lesion area. If the imagehas a lesion area (Yes), the image may be stored in operation S280. Ifthe image does not have the lesion area (No), operation S260 may beperformed again.

The capsule endoscope according to the embodiment of the inventiveconcept may reduce power consumption by transmitting only an imagehaving a lesion area.

A receiving device according to an embodiment of the inventive conceptmay store only an image having a lesion area using a flag bit. Thus, theamount of image stored in the receiving device may be reduced. Further,since the amount of the image to be determined in determining the imageis reduced, the determining time may be reduced.

Although the exemplary embodiments of the inventive concept have beendescribed, it is understood that the inventive concept should not belimited to these exemplary embodiments but various changes andmodifications can be made by one ordinary skilled in the art within thespirit and scope of the inventive concept as hereinafter claimed.

What is claimed is:
 1. A capsule endoscope comprising: an imaging deviceconfigured to perform imaging on a digestive tract in a living body togenerate an image; an artificial neural network configured to determinewhether there is a lesion area in the image; and a transmitterconfigured to transmit the image based on a determination result of theartificial neural network.
 2. The capsule endoscope of claim 1, whereinthe transmitter generates a flag bit indicating the determination resultof the artificial neural network, and transmits the flag bit togetherwith the image.
 3. The capsule endoscope of claim 1, wherein theartificial neural network is based on convolution neural network (CNN).4. The capsule endoscope of claim 3, wherein the artificial neuralnetwork stores a kernel matrix for determining pixels in the image and aweight indicating a degree of coupling of neurons in the CNN.
 5. Thecapsule endoscope of claim 4, wherein the kernel matrix and the weightare previously learned data.
 6. The capsule endoscope of claim 5,wherein the kernel matrix and the weight are updated by the artificialneural network.
 7. The capsule endoscope of claim 1, wherein theartificial neural network activates the transmitter if the lesion areais present in the image and generates a control signal for deactivatingthe transmitter if the lesion area is not present in the image.
 8. Thecapsule endoscope of claim 7, further comprising: a power supply circuitconfigured to supply power to the imaging device, the artificial neuralnetwork, and the transmitter; and a switch configured to connect thepower supply circuit and the transmitter.
 9. The capsule endoscope ofclaim 8, wherein the switch connects the power supply circuit and thetransmitter in accordance with the control signal.
 10. The capsuleendoscope of claim 1, wherein the artificial neural network is based onspiking neural network (SNN).
 11. The capsule endoscope of claim 10,wherein the artificial neural network a weight indicating a degree ofcoupling of neurons in the SNN.
 12. A receiving device comprising: areceiver configured to receive from a capsule endoscope an image of adigestive tract in a living body and a flag bit indicating whether thereis a lesion area in the image; a decoder configured to decode the flagbit and determine whether to store the image; and a storage deviceconfigured to store the image in accordance with a decoding result ofthe decoder.
 13. The receiving device of claim 12, wherein the decoderdecodes the flag bit before the image is stored in the storage device todetermine whether the lesion area is present in the image.
 14. Thereceiving device of claim 13, wherein if the lesion area is present inthe image, the image is stored in the storage device, and if the lesionarea is not present in the image, the image is not stored in the storagedevice.
 15. The receiving device of claim 12, wherein the flag bitindicates a result that the artificial neural network of the capsuleendoscope determines whether the lesion area is present in the image.